Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating personalized medicine (PMed) trials.
Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating personalized medicine (PMed) trials.
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DOI：
10.1371/journal.pone.0090028
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Trent J
中科院分区：
文献类型：
--
作者：
Paoloni M;Webb C;Mazcko C;Cherba D;Hendricks W;Lana S;Ehrhart EJ;Charles B;Fehling H;Kumar L;Vail D;Henson M;Childress M;Kitchell B;Kingsley C;Kim S;Neff M;Davis B;Khanna C;Trent J
分子引导试验（即PMed）现在试图通过将癌症靶点与治疗方案相匹配来帮助临床决策。由于缺乏癌症模型来解释癌症类型内部和之间的个体与个体异质性，进展受到阻碍。宠物动物中自然发生的癌症是异质性的，因此提供了一个机会来回答有关这些PMed策略的问题，并优化向人类患者的转化。为了实现这一机会，现在有必要证明在临床相关环境中对患有自然发生癌症的犬的肿瘤进行分子引导分析的可行性。比较肿瘤学试验联盟（COTC）进行了一项概念验证研究，以确定在1周内在犬中进行肿瘤采集、前瞻性分子谱分析和PMed报告生成是否可行。入组了31只患有不同组织学癌症的犬。31个样品中的24个（77%）成功地满足所有预定义的QA/QC标准，并通过Affyssin基因表达谱进行分析。随后的生物信息学工作流程将基因组数据转化为个性化的药物报告。从活检到生成报告的平均周转时间为116小时（4.8天）。犬肿瘤表达数据的无监督聚类按癌症类型聚类，但肿瘤的有监督聚类基于按药物类别而不是癌症类型聚类的个性化药物报告。高质量犬肿瘤样本的收集和周转、集中病理学、分析物生成、阵列杂交和将基因表达与治疗选择匹配的生物信息学分析在实际临床窗口（<1周）内可实现。聚类数据显示了癌症类型的强大特征，但也显示了药物预测的患者间异质性。这进一步支持将患有癌症的狗的异质群体纳入个性化医学的临床前建模。未来的比较肿瘤学研究优化PMed策略的交付可能有助于癌症药物的开发。
Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.
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